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In the BSDLab, we investigate novel algorithmic decoding strategies, which allow us to better understand and utilize high-dimensional, noisy and multimodal brain signals. The decoding of brain signals in single-trial involves heavy use of machine learning methods. Successful decoding approaches are implemented in closed loop applications like brain-computer interfaces (BCIs), which allow for novel interaction paradigms with the brain in stroke rehabilitation, to establish communication and control, to improve human-robot interaction and test novel neuro-ergonomic interaction paradigms. Currently funded projects comprise:


BCI neurotechnology for novel rehabilitation approaches in stroke-induced cognitive deficits: Evaluation of brain-computer interface methods for training language deficits (aphasia) following a brain stroke.

CogReha - Read More…


Individualized hand motor training under suitable brain states to improve performance and learning after stroke

SuitAble - Read More…